Deep Reinforcement Learning for Scheduling Applications in Serverless and Serverful Hybrid Computing Environments

Abstract

Serverless computing has gained popularity as a novel cloud execution model for applications in recent times. Businesses constantly try to leverage this new paradigm to add value to their revenue streams. The serverless eco-system accommodates many application domains successfully. However, its inherent properties such as cold start delays and relatively high per unit charges appear as a shortcoming for certain application workloads, when compared to a traditional Virtual Machine (VM) based execution scenario. A few research works exist, that study how serverless computing could be used to mitigate the challenges in a VM based cluster environment, for certain applications. In contrast, this work proposes a generalized framework for determining which workloads are best able to reap benefits of a serverless computing environment. In essence, we present a potential hybrid scheduling solution for exploiting the benefits of both a serverless and a VM based serverful computing environment. Our proposed framework leverages the actor-critic based deep reinforcement learning architecture coupled with the proximal policy optimization technique, in determining the best scheduling decision for workload executions. Extensive experiments conducted demonstrate the effectiveness of such a solution, in terms of user cost and application performance, with improvements of up to 44% and 11% respectively.